


'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFLEDForConditionalGeneration, TFLEDModel





@require_tf
class        lowercase__    :





							'''simple docstring'''


							A_				: List[str]       =    LEDConfig
							A_				: Any       =    {}
							A_				: Optional[int]       =    """gelu"""






							def __init__(       self       ,   __snake_case       ,   __snake_case=13       ,   __snake_case=7       ,   __snake_case=True       ,   __snake_case=False       ,   __snake_case=99       ,   __snake_case=32       ,   __snake_case=2       ,   __snake_case=4       ,   __snake_case=37       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=20       ,   __snake_case=2       ,   __snake_case=1       ,   __snake_case=0       ,   __snake_case=4       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= parent
											_SCREAMING_SNAKE_CASE					:			str							= batch_size
											_SCREAMING_SNAKE_CASE					:			List[str]							= seq_length
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= is_training
											_SCREAMING_SNAKE_CASE					:			int							= use_labels
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			List[str]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			List[Any]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= intermediate_size

											_SCREAMING_SNAKE_CASE					:			List[Any]							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			int							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			List[Any]							= eos_token_id
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pad_token_id
											_SCREAMING_SNAKE_CASE					:			int							= bos_token_id
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= attention_window

											# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
											# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
											# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
											# because its local attention only attends to `self.attention_window` and one before and one after
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.attention_window + 2

											# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
											# the `test_attention_outputs` and `test_hidden_states_output` tests
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= (
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= ids_tensor([self.batch_size, self.seq_length - 1]       ,   self.vocab_size    )
											_SCREAMING_SNAKE_CASE					:			str							= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )       ,   1    )
											_SCREAMING_SNAKE_CASE					:			Dict							= tf.concat([input_ids, eos_tensor]       ,   axis=1    )

											_SCREAMING_SNAKE_CASE					:			int							= ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= self.config_cls(
											    vocab_size=self.vocab_size       ,   d_model=self.hidden_size       ,   encoder_layers=self.num_hidden_layers       ,   decoder_layers=self.num_hidden_layers       ,   encoder_attention_heads=self.num_attention_heads       ,   decoder_attention_heads=self.num_attention_heads       ,   encoder_ffn_dim=self.intermediate_size       ,   decoder_ffn_dim=self.intermediate_size       ,   dropout=self.hidden_dropout_prob       ,   attention_dropout=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   eos_token_ids=[2]       ,   bos_token_id=self.bos_token_id       ,   pad_token_id=self.pad_token_id       ,   decoder_start_token_id=self.pad_token_id       ,   attention_window=self.attention_window       ,   **self.config_updates       ,   )
											_SCREAMING_SNAKE_CASE					:			Dict							= prepare_led_inputs_dict(__snake_case       ,   __snake_case       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= tf.concat(
											    [tf.zeros_like(__snake_case    )[:, :-1], tf.ones_like(__snake_case    )[:, -1:]]       ,   axis=-1       ,   )
											_SCREAMING_SNAKE_CASE					:			Dict							= global_attention_mask
											return config, inputs_dict






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= TFLEDModel(config=__snake_case    ).get_decoder()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= inputs_dict["""input_ids"""]

											_SCREAMING_SNAKE_CASE					:			List[str]							= input_ids[:1, :]
											_SCREAMING_SNAKE_CASE					:			str							= inputs_dict["""attention_mask"""][:1, :]
											_SCREAMING_SNAKE_CASE					:			List[str]							= 1

											# first forward pass
											_SCREAMING_SNAKE_CASE					:			Any							= model(__snake_case       ,   attention_mask=__snake_case       ,   use_cache=__snake_case    )

											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											_SCREAMING_SNAKE_CASE					:			str							= ids_tensor((self.batch_size, 3)       ,   config.vocab_size    )
											_SCREAMING_SNAKE_CASE					:			Dict							= tf.cast(ids_tensor((self.batch_size, 3)       ,   2    )       ,   tf.inta    )

											# append to next input_ids and
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tf.concat([input_ids, next_tokens]       ,   axis=-1    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= tf.concat([attention_mask, next_attn_mask]       ,   axis=-1    )

											_SCREAMING_SNAKE_CASE					:			Any							= model(__snake_case       ,   attention_mask=__snake_case    )[0]
											_SCREAMING_SNAKE_CASE					:			List[str]							= model(__snake_case       ,   attention_mask=__snake_case       ,   past_key_values=__snake_case    )[0]

											self.parent.assertEqual(next_tokens.shape[1]       ,   output_from_past.shape[1]    )

											# select random slice
											_SCREAMING_SNAKE_CASE					:			str							= int(ids_tensor((1,)       ,   output_from_past.shape[-1]    )    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= output_from_no_past[:, -3:, random_slice_idx]
											_SCREAMING_SNAKE_CASE					:			Tuple							= output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(__snake_case       ,   __snake_case       ,   rtol=1e-3    )







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, ):







				"""simple docstring"""





				if attention_mask is None:
								_SCREAMING_SNAKE_CASE					:			Any							= tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__				, config.pad_token_id	)				, tf.inta	)
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape				, dtype=tf.inta	),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				, config.pad_token_id	)				, tf.inta	),
								    ]				, axis=-1				, )
				if head_mask is None:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				    "decoder_input_ids": decoder_input_ids,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				}





@require_tf
class        lowercase__    (		_snake_case	,	_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: List[Any]       =    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							A_				: List[str]       =    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
							A_				: Optional[Any]       =    (
							    {
							        """conversational""": TFLEDForConditionalGeneration,
							        """feature-extraction""": TFLEDModel,
							        """summarization""": TFLEDForConditionalGeneration,
							        """text2text-generation""": TFLEDForConditionalGeneration,
							        """translation""": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							A_				: Dict       =    True
							A_				: str       =    False
							A_				: List[Any]       =    False
							A_				: Dict       =    False






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= TFLEDModelTester(self    )
											_SCREAMING_SNAKE_CASE					:			Any							= ConfigTester(self       ,   config_class=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											self.config_tester.run_common_tests()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE					:			Tuple							= tf.zeros_like(inputs_dict["""attention_mask"""]    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= 2
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tf.where(
											    tf.range(self.model_tester.seq_length    )[None, :] < num_global_attn_indices       ,   1       ,   inputs_dict["""global_attention_mask"""]       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= True
											_SCREAMING_SNAKE_CASE					:			int							= self.model_tester.seq_length
											_SCREAMING_SNAKE_CASE					:			str							= self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= outputs.decoder_attentions
															self.assertEqual(len(__snake_case    )       ,   self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(decoder_attentions[0].shape[-3:]    )       ,   [self.model_tester.num_attention_heads, seq_length, seq_length]       ,   )

											def check_encoder_attentions_output(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			str							= [t.numpy() for t in outputs.encoder_attentions]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [t.numpy() for t in outputs.encoder_global_attentions]
															self.assertEqual(len(__snake_case    )       ,   self.model_tester.num_hidden_layers    )
															self.assertEqual(len(__snake_case    )       ,   self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(attentions[0].shape[-3:]    )       ,   [self.model_tester.num_attention_heads, seq_length, seq_length]       ,   )
															self.assertListEqual(
															    list(global_attentions[0].shape[-3:]    )       ,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]       ,   )

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE					:			Dict							= True
															_SCREAMING_SNAKE_CASE					:			Any							= False
															_SCREAMING_SNAKE_CASE					:			List[str]							= False
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model_class(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			List[Any]							= model(self._prepare_for_class(__snake_case       ,   __snake_case    )    )
															_SCREAMING_SNAKE_CASE					:			List[Any]							= len(__snake_case    )
															self.assertEqual(config.output_hidden_states       ,   __snake_case    )
															check_encoder_attentions_output(__snake_case    )

															if self.is_encoder_decoder:
																			_SCREAMING_SNAKE_CASE					:			Any							= model_class(__snake_case    )
																			_SCREAMING_SNAKE_CASE					:			List[Any]							= model(self._prepare_for_class(__snake_case       ,   __snake_case    )    )
																			self.assertEqual(config.output_hidden_states       ,   __snake_case    )
																			check_decoder_attentions_output(__snake_case    )

															# Check that output attentions can also be changed via the config
															del inputs_dict["output_attentions"]
															_SCREAMING_SNAKE_CASE					:			int							= True
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model_class(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Tuple							= model(self._prepare_for_class(__snake_case       ,   __snake_case    )    )
															self.assertEqual(config.output_hidden_states       ,   __snake_case    )
															check_encoder_attentions_output(__snake_case    )

															# Check attention is always last and order is fine
															_SCREAMING_SNAKE_CASE					:			List[str]							= True
															_SCREAMING_SNAKE_CASE					:			Any							= True
															_SCREAMING_SNAKE_CASE					:			int							= model_class(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Any							= model(self._prepare_for_class(__snake_case       ,   __snake_case    )    )

															self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)       ,   len(__snake_case    )    )
															self.assertEqual(model.config.output_hidden_states       ,   __snake_case    )
															check_encoder_attentions_output(__snake_case    )






							@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""    )
							def UpperCAmelCase_      (       self    ):
											pass






							def UpperCAmelCase_      (       self    ):
											# TODO: Head-masking not yet implement
											pass







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return tf.constant(SCREAMING_SNAKE_CASE__				, dtype=tf.intaa	)


UpperCAmelCase_						:  Union[str, Any]				  =						1E-4







@slow
@require_tf
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""    ).led

											# change to intended input here
											_SCREAMING_SNAKE_CASE					:			int							= _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
											_SCREAMING_SNAKE_CASE					:			int							= _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= prepare_led_inputs_dict(model.config       ,   __snake_case       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model(**__snake_case    )[0]
											_SCREAMING_SNAKE_CASE					:			Dict							= (1, 1024, 768)
											self.assertEqual(output.shape       ,   __snake_case    )
											# change to expected output here
											_SCREAMING_SNAKE_CASE					:			Any							= tf.convert_to_tensor(
											    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]       ,   )
											tf.debugging.assert_near(output[:, :3, :3]       ,   __snake_case       ,   atol=1e-3    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""    )

											# change to intended input here
											_SCREAMING_SNAKE_CASE					:			Any							= _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
											_SCREAMING_SNAKE_CASE					:			Dict							= _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= prepare_led_inputs_dict(model.config       ,   __snake_case       ,   __snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= model(**__snake_case    )[0]
											_SCREAMING_SNAKE_CASE					:			Any							= (1, 1024, model.config.vocab_size)
											self.assertEqual(output.shape       ,   __snake_case    )
											# change to expected output here
											_SCREAMING_SNAKE_CASE					:			List[Any]							= tf.convert_to_tensor(
											    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]       ,   )
											tf.debugging.assert_near(output[:, :3, :3]       ,   __snake_case       ,   atol=1e-3       ,   rtol=1e-3    )








'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_						:  List[str]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[Any]				  =						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_						:  Tuple				  =						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for attribute in key.split("""."""	):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE					:			int							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	).shape
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
								    f""" {value.shape} for {full_name}"""	)

				if weight_type == "weight":
								_SCREAMING_SNAKE_CASE					:			int							= value
				elif weight_type == "weight_g":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				elif weight_type == "weight_v":
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= value
				elif weight_type == "bias":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_mean":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_var":
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value
				elif weight_type == "num_batches_tracked":
								_SCREAMING_SNAKE_CASE					:			Any							= value
				elif weight_type == "inv_freq":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				else:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= value

				logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE					:			Any							= hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False
								if "conv_layers" in name:
												load_conv_layer(
												    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, hf_model.config.feat_extract_norm == """group"""				, )
												_SCREAMING_SNAKE_CASE					:			str							= True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_SNAKE_CASE					:			List[Any]							= """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
																				_SCREAMING_SNAKE_CASE					:			List[str]							= True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= name.split(SCREAMING_SNAKE_CASE__	)[0].split("""."""	)[-2]
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= mapped_key.replace("""*"""				, SCREAMING_SNAKE_CASE__	)
																				if "pos_bias_u" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= None
																				elif "pos_bias_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[int]							= None
																				elif "weight_g" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """weight_g"""
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Any							= """weight_v"""
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE					:			Dict							= """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_SCREAMING_SNAKE_CASE					:			int							= """weight"""
																				elif "running_mean" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """running_mean"""
																				elif "inv_freq" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """inv_freq"""
																				elif "running_var" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """running_var"""
																				elif "num_batches_tracked" in name:
																								_SCREAMING_SNAKE_CASE					:			Tuple							= """num_batches_tracked"""
																				else:
																								_SCREAMING_SNAKE_CASE					:			Dict							= None
																				set_recursively(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
																continue
								if not is_used:
												unused_weights.append(SCREAMING_SNAKE_CASE__	)

				logger.warning(f"""Unused weights: {unused_weights}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= full_name.split("""conv_layers."""	)[-1]
				_SCREAMING_SNAKE_CASE					:			Tuple							= name.split("""."""	)
				_SCREAMING_SNAKE_CASE					:			Any							= int(items[0]	)
				_SCREAMING_SNAKE_CASE					:			str							= int(items[1]	)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			str							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__	)







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=True	):







				"""simple docstring"""





				if config_path is not None:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE__				, hidden_act="""swish"""	)
				else:
								_SCREAMING_SNAKE_CASE					:			List[str]							= WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								_SCREAMING_SNAKE_CASE					:			List[str]							= """rotary"""

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE					:			List[str]							= Dictionary.load(SCREAMING_SNAKE_CASE__	)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= target_dict.pad_index
												_SCREAMING_SNAKE_CASE					:			int							= target_dict.bos_index
												_SCREAMING_SNAKE_CASE					:			Any							= target_dict.eos_index
												_SCREAMING_SNAKE_CASE					:			List[str]							= len(target_dict.symbols	)
												_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(SCREAMING_SNAKE_CASE__				, """vocab.json"""	)
												if not os.path.isdir(SCREAMING_SNAKE_CASE__	):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__	)	)
																return
												os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Dict							= target_dict.indices

												# fairseq has the <pad> and <s> switched
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
												_SCREAMING_SNAKE_CASE					:			List[Any]							= 1
												with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as vocab_handle:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= WavaVecaCTCTokenizer(
												    SCREAMING_SNAKE_CASE__				, unk_token=target_dict.unk_word				, pad_token=target_dict.pad_word				, bos_token=target_dict.bos_word				, eos_token=target_dict.eos_word				, word_delimiter_token="""|"""				, do_lower_case=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= True if config.feat_extract_norm == """layer""" else False
												_SCREAMING_SNAKE_CASE					:			Dict							= WavaVecaFeatureExtractor(
												    feature_size=1				, sampling_rate=1_6000				, padding_value=0				, do_normalize=SCREAMING_SNAKE_CASE__				, return_attention_mask=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				, tokenizer=SCREAMING_SNAKE_CASE__	)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE					:			str							= WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE__	)
				else:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE__	)

				if is_finetuned:
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]				, arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
				else:
								_SCREAMING_SNAKE_CASE					:			Tuple							= argparse.Namespace(task="""audio_pretraining"""	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				, task=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			Optional[int]							= model[0].eval()

				recursively_load_weights(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, not is_finetuned	)

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				convert_wavaveca_conformer_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)




